Last updated: 2021-03-10
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Knit directory: fastTopics-experiments/analysis/
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Here we prepare the NIPS 1–17 data for subsequent topic modeling analyses. To run these data preparation steps, download the data from here then save the downloaded MAT file as “nips.mat” inside the “data” directory. Note that it may be necessary to re-save the data in MATLAB by running save nips.mat -v6 so that it can be successfully loaded by the readMat function from the R.matlab package:
The R.matlab package is used to read the MATLAB data structures into R.
library(Matrix)
library(R.matlab)Documents with fewer than two nonzero counts are removed.
dat <- readMat("../data/nips_1-17.mat")
counts <- t(dat$counts)
rownames(counts) <- unlist(dat$docs.names)
colnames(counts) <- unlist(dat$words)
rows <- which(rowSums(counts > 0) > 1)
counts <- counts[rows,]The word counts data should be stored as a 2,483 x 14,036 matrix. Approximately 4% of the word counts are positive.
n <- nrow(counts)
m <- ncol(counts)
cat(sprintf("Number of documents: %d\n",n))
cat(sprintf("Number of words: %d\n",m))
cat(sprintf("Rate of nonzero counts: %0.2f%%\n",100*nnzero(counts)/(n*m)))
# Number of documents: 2483
# Number of words: 14036
# Rate of nonzero counts: 3.74%Among the counts that are positive, the vast majority are small.
cat("The word counts are mostly small, with a small number of large counts:\n")
print(quantile(summary(counts)$x,c(0,0.5,0.9,0.99,0.999,1)))
# The word counts are mostly small, with a small number of large counts:
# 0% 50% 90% 99% 99.9% 100%
# 1 1 5 21 46 160Write the counts to an RData file.
save(list = "counts",file = "nips.RData")
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] R.matlab_3.6.2 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] Rcpp_1.0.5 knitr_1.26 whisker_0.4
# [4] magrittr_1.5 workflowr_1.6.2.9000 lattice_0.20-38
# [7] R6_2.4.1 rlang_0.4.5 stringr_1.4.0
# [10] tools_3.6.2 grid_3.6.2 xfun_0.11
# [13] R.oo_1.23.0 git2r_0.26.1 htmltools_0.4.0
# [16] yaml_2.2.0 digest_0.6.23 rprojroot_1.3-2
# [19] later_1.0.0 R.utils_2.9.2 promises_1.1.0
# [22] fs_1.3.1 glue_1.3.1 evaluate_0.14
# [25] rmarkdown_2.3 stringi_1.4.3 compiler_3.6.2
# [28] backports_1.1.5 R.methodsS3_1.7.1 httpuv_1.5.2